Base Compute
Base Compute provides a specialized local AI runtime that maximizes inference performance on hardware organizations already own, eliminating cloud dependency and per-token costs. Its BaseRT engine delivers prefill speeds up to 6.4x faster than llama.cpp on Apple Silicon, and the company publishes a monthly State of Local AI report mapping model classes to hardware tiers and realistic token speeds.
- Artificial Intelligence
- Developer Tools
- Software Only
- AI Agents
Funding
Founders
Founder details are not available yet.
Product
Problem
Running large language models in the cloud introduces latency, privacy risks, and recurring per-token costs, while existing on-device runtimes like llama.cpp and MLX fail to fully exploit the hardware's capabilities. Organizations and individuals seeking private, low-cost AI inference are constrained by slow prefill and decode speeds that make local deployment impractical for real-time or agentic workloads.
Solution
Base Compute provides a specialized local AI runtime that maximizes inference performance on hardware users already own, such as laptops, workstations, and on-premises servers. Its BaseRT engine achieves prefill speeds up to 6.4x faster than llama.cpp and 3.9x faster than MLX on Apple Silicon, with decode up to 1.33x faster than MLX, making local inference viable for real-time and agentic workloads. The runtime auto-optimizes for each machine, enabling instant setup with no cloud, no accounts, and zero cost per token. For enterprises, Base Compute offers the Base Optimisation Stack (B:OS), a full-stack tuning approach that optimizes every layer from model to kernel to silicon, delivering day-0 support for new model releases across Apple, NVIDIA, AMD, and other accelerators. The company also publishes a monthly State of Local AI report that maps model classes to hardware tiers and realistic token-per-second speeds, helping organizations size their local AI deployments.
Target Audience
Primary customers are enterprises seeking to run AI within their own environments, as well as model makers, chip manufacturers, and device makers shipping AI at scale who need ultra-optimized on-device inference.
Features
- Prefill acceleration up to 6.4x faster than llama.cpp and 3.9x faster than MLX on Apple Silicon
- Decode performance up to 1.33x faster than MLX, enabling smoother real-time token generation
- Auto-optimized setup for each machine, requiring no manual configuration or cloud connectivity
- Base Optimisation Stack (B:OS) for enterprises, tuning model, kernel, and silicon layers for maximum hardware utilization
- Cross-accelerator support including Apple Silicon, NVIDIA, AMD, and other accelerators
- Day-0 compatibility with new model releases, ensuring immediate access to the latest open-weight models
- Deployment team evaluates existing AI usage and workflows to set up private agents that operate locally and remain tuned as needs grow